Carbon emission reduction based power allocation protocol simulation optimization method, system and apparatus

Abstract

The application relates to the technical field of power allocation protocol simulation optimization, and discloses a power allocation protocol simulation optimization method, system and device based on carbon emission reduction, which comprises the following steps: day-ahead decomposition is performed on configuration information to obtain time sequence output data of a day-ahead scheduling plan; carbon flow accounting is performed on a power system according to a worst scenario of power allocation protocol execution to obtain the maximum carbon emission of a power consumption side subject; and simulation optimization is performed on the configuration information according to the time sequence output data and a multi-level protocol simulation optimization model to obtain a simulation configuration strategy. Through a robust optimization day-ahead plan decomposition method, the stability of power allocation protocol performance is improved; through a multi-level protocol simulation optimization model, the accuracy of simulation results is improved, so that the feasibility of the power allocation protocol is improved; through carbon flow accounting for carbon emission allocation, the actual contribution of each subject to carbon emission reduction is quantified, carbon emission reduction optimization of the power system is realized, and low-carbon transformation of the power system is promoted.

Description

Technical Field

[0001] This invention relates to the field of power configuration protocol simulation and optimization technology, and in particular to a power configuration protocol simulation and optimization method, system and device based on carbon emission reduction. Background Technology

[0002] With the large-scale integration of renewable energy and the increasing complexity of power system operation, traditional deterministic power allocation methods and carbon emission accounting techniques can no longer meet the increasingly complex system optimization needs. The main technical challenges currently facing power system carbon reduction allocation optimization are twofold: firstly, the high uncertainty of renewable energy output and electricity load leads to a lack of flexibility in traditional power allocation agreements, making it difficult for the system to balance technical feasibility and operational stability when implementing green power allocation; secondly, the lack of precise technical quantification methods for defining carbon emission responsibility in power allocation results in inaccurate assessments of carbon reduction effects.

[0003] Currently, the main technical solutions for addressing the uncertainties in power configuration agreements and carbon emission reduction optimization include deterministic optimization methods, stochastic optimization methods, and traditional game theory methods. However, these methods all have certain limitations. Although deterministic optimization methods are computationally efficient, they cannot handle the uncertainties in renewable energy output and load, resulting in poor technical feasibility of the optimization results in actual implementation and high system operation risks. Stochastic optimization methods require accurate probability distribution information as input, but it is difficult to obtain accurate probability models in practical applications, and the computational complexity is high. Traditional game theory methods are mainly for single-objective optimization in deterministic environments and lack the ability to systematically handle multi-objective coordination and uncertainties. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method, system, and device for simulating and optimizing power configuration protocols based on carbon emission reduction. By simulating and optimizing power configuration protocols, the robustness and executability of power configuration protocols are improved, thereby enhancing the stability of power system operation and carbon emission reduction.

[0005] In a first aspect, the present invention provides a simulation optimization method for power configuration protocols based on carbon emission reduction, the method comprising: The configuration information of the power configuration protocol to be simulated is obtained, and the configuration information is decomposed day-ahead to obtain the time-series output data of the day-ahead scheduling plan. The main body of the power configuration protocol includes the power consumption side body and the power generation side body. The power generation side body includes the thermal power generation body and the green power generation body. Based on the worst-case scenario executed according to the preset power configuration protocol, carbon flow accounting is performed on the power system to obtain the maximum carbon emissions of the main electricity consumer. Based on the time-series output data and the preset multi-level protocol simulation optimization model, the configuration information is simulated and optimized to obtain a simulation configuration strategy. The multi-level protocol simulation optimization model takes minimizing the load deviation and carbon emissions of the electricity-consuming entity, maximizing the compliance of the thermal power generation entity, and maximizing the compliance of the green power generation entity as optimization objectives, and takes carbon emission quota constraints based on the maximum carbon emissions, electricity-consuming load operation constraints, thermal power generation operation constraints, and green power generation operation constraints as constraints.

[0006] Furthermore, the step of obtaining the configuration information of the power configuration protocol to be simulated, and performing day-ahead decomposition on the configuration information to obtain the time-series output data of the day-ahead scheduling plan includes: Obtain the configuration information of the power configuration protocol to be simulated, including the total power consumption of the protocol, the power consumption ratio of the protocol, and the load demand of the protocol; Calculate the green electricity agreement amount and the thermal power agreement amount based on the total electricity amount under the agreement and the electricity proportion under the agreement; Based on the agreed load demand, a day-ahead decomposition method based on adjustment coefficients is used to decompose the green electricity and thermal power agreed quantities to obtain the time-series output data of the day-ahead scheduling plan. The time-series output data includes green electricity time-series output data and thermal power time-series output data, and the adjustment coefficients satisfy the balance constraints.

[0007] Furthermore, the step of calculating the carbon flow of the power system based on the worst-case scenario executed according to the preset power configuration protocol to obtain the maximum carbon emissions of the electricity-consuming entity includes: Based on the load data of the power consumption side and the output data of the power generation side under the worst-case scenario, power flow calculation is performed on the actual power network of the power system to obtain the power flow distribution. Based on the power flow distribution, a countercurrent power flow tracking algorithm is used to distribute the carbon emissions of the power generation entity to the power consumption entity, thereby obtaining the basic total carbon emissions of the power consumption entity under the worst-case scenario, and using the basic total carbon emissions as the maximum carbon emissions of the power consumption entity.

[0008] Furthermore, after the step of obtaining the base carbon emissions of the electricity-consuming entity under the worst-case scenario, the method further includes: Based on the difference between the load data of the power consumption side and the output data of the power generation side under the worst-case scenario and the corresponding historical load data and historical output data, a deviation network of the actual power network is established. Power flow calculation is performed on the deviation network, and based on the calculated power flow distribution, the reverse power flow tracking algorithm is used to distribute the deviation carbon emissions of the power generation side to the power consumption side, thereby obtaining the deviation carbon emissions of the power consumption side. The sum of the baseline total carbon emissions and the deviation carbon emissions shall be taken as the maximum carbon emissions of the electricity-consuming entity.

[0009] Furthermore, the multi-level protocol simulation optimization model adopts a hybrid game model, which is constructed with the electricity-consuming entity as the leader and the power generation entity as the follower, including the electricity-consuming side protocol simulation model, the thermal power side protocol simulation model, and the green power side protocol simulation model; The electricity consumption side protocol simulation model takes minimizing the load deviation and carbon emissions of the electricity consumption side as the objective function, and carbon emission limit constraints and electricity consumption side load operation constraints as constraints. The electricity consumption side load operation constraints include power load balance constraints, load boundary constraints and load fluctuation constraints. The thermal power side agreement simulation model takes maximizing the performance of the thermal power generation entity as the objective function and thermal power generation operation constraints as the constraint conditions. The thermal power generation operation constraints include unit operation constraints, ramp-up constraints, and minimum start-up and shutdown constraints. The green electricity side agreement simulation model takes maximizing the performance of the green electricity generation entity as the objective function and green electricity generation operation constraints as the constraint conditions. The green electricity generation operation constraints include the upper limit constraint on output and the total green electricity generation constraint.

[0010] Furthermore, the steps for constructing the power consumption-side protocol simulation model include: Based on the agreed load demand, historical load demand, and load deviation tolerance threshold, the load deviation of the electricity consumption entity is obtained; Based on the agreed load demand and the time-series output data, carbon flow accounting and carbon emission allocation are performed on the power system to obtain the carbon emissions of the main electricity consumer. A simulation model of the electricity consumption protocol is constructed with the objective functions of minimizing the load deviation and the carbon emissions.

[0011] Furthermore, the steps for constructing the thermal power-side protocol simulation model include: The reliability items for thermal power performance are calculated based on the difference between the historical output data of the main thermal power generation entity and the time-series output data of thermal power in the day-ahead dispatch plan. Based on the preset thermal power performance tolerance coefficient and the thermal power time-series output data of the day-ahead dispatch plan, the thermal power tolerance time-series output data is calculated, and the thermal power performance shortfall is calculated based on the difference between the thermal power tolerance time-series output data and the historical output data of the thermal power generation entity. The difference between the reliable thermal power performance items and the unsatisfactory thermal power performance items is taken as the performance level of the thermal power generation entity. A thermal power side protocol simulation model is constructed with maximizing the performance level of the thermal power generation entity as the objective function.

[0012] Furthermore, the steps for constructing the green electricity side protocol simulation model include: The reliability items for green power performance are calculated based on the difference between the historical output data of the main green power generation entity and the time-series output data of green power in the day-ahead dispatch plan. Based on the preset green electricity performance tolerance coefficient and the green electricity time-series output data of the day-ahead dispatch plan, calculate the green electricity tolerance time-series output data, and calculate the green electricity performance shortfall based on the difference between the green electricity tolerance time-series output data and the historical output data of the green electricity power generation entity. The difference between the reliable green electricity performance items and the insufficient green electricity performance items is taken as the performance level of the green electricity generation entity. A green electricity side protocol simulation model is constructed with the objective function of maximizing the performance level of the green electricity generation entity.

[0013] Secondly, the present invention provides a power configuration protocol simulation optimization system based on carbon emission reduction, the system comprising: The day-ahead decomposition module is used to obtain the configuration information of the power configuration protocol to be simulated, and to perform day-ahead decomposition on the configuration information to obtain the time-series output data of the day-ahead scheduling plan. The protocol body of the power configuration protocol includes the power consumption side body and the power generation side body. The power generation side body includes the thermal power generation body and the green power generation body. The carbon flow calculation module is used to perform carbon flow calculation on the power system based on the worst-case scenario executed according to the preset power configuration protocol, so as to obtain the maximum carbon emissions of the main body on the power consumption side. The protocol simulation module is used to perform simulation optimization on the configuration information based on the time-series output data, the worst-case scenario, and a preset multi-level protocol simulation optimization model to obtain a simulation configuration strategy. The multi-level protocol simulation optimization model takes minimizing the load deviation and carbon emissions of the electricity-consuming entity, maximizing the compliance of the thermal power generation entity, and maximizing the compliance of the green power generation entity as optimization objectives, and takes carbon emission quota constraints based on the maximum carbon emissions, electricity-consuming load operation constraints, thermal power generation operation constraints, and green power generation operation constraints as constraints.

[0014] Thirdly, embodiments of the present invention also provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0015] This invention provides a simulation optimization method, system, and device for power configuration agreements based on carbon emission reduction. Through a robust day-ahead planning decomposition method, this invention improves the stability of power configuration agreement performance. By establishing a multi-level agreement simulation optimization model, it accurately models the strategic interactions among multiple power entities and the actual operating characteristics of the power market, thereby enhancing the feasibility of power configuration agreements. Furthermore, by using carbon flow accounting for carbon emission allocation, it quantifies the actual contribution of each entity to carbon emission reduction and incorporates carbon emissions into the optimization objective of the simulation model, achieving carbon emission reduction optimization of the power system and promoting its low-carbon transformation. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the simulation optimization method for power configuration protocols based on carbon emission reduction in this embodiment of the invention. Figure 2 This is a schematic diagram of the structure of the power configuration protocol simulation and optimization system based on carbon emission reduction in this embodiment of the invention; Figure 3 This is an internal structural diagram of the computer device in an embodiment of the present invention.

[0017] Figure label: 10. Day-to-day decomposition module; 20. Carbon flow accounting module; 30. Protocol simulation module. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Please see Figure 1 The first embodiment of the present invention proposes a simulation optimization method for power configuration protocols based on carbon emission reduction, including steps S10 to S30: Step S10: Obtain the configuration information of the power configuration protocol to be simulated, perform day-ahead decomposition on the configuration information, and obtain the time-series output data of the day-ahead scheduling plan. The main body of the power configuration protocol includes the power consumption side main body and the power generation side main body. The power generation side main body includes the thermal power generation main body and the green power generation main body. Step S20: Based on the worst-case scenario executed by the preset power configuration protocol, perform carbon flow calculation on the power system to obtain the maximum carbon emissions of the main electricity consumer. Step S30: Based on the time-series output data and the preset multi-level protocol simulation optimization model, the configuration information is simulated and optimized to obtain a simulation configuration strategy. The multi-level protocol simulation optimization model takes minimizing the load deviation and carbon emissions of the electricity-consuming entity, maximizing the compliance of the thermal power generation entity, and maximizing the compliance of the green power generation entity as optimization objectives, and takes carbon emission quota constraints based on the maximum carbon emissions, electricity-consuming load operation constraints, thermal power generation operation constraints, and green power generation operation constraints as constraints.

[0020] In this embodiment, the power configuration agreement refers to a monthly or annual power configuration agreement between an electricity-consuming enterprise and a power plant. The main parties to the agreement include the electricity-consuming entity and the power generation entity (such as a thermal power generation entity and a green power generation entity). The electricity-consuming entity is the electricity-consuming enterprise, and the power generation entity is the power plant, including thermal power generation entities (thermal power plants) and green power generation entities (renewable energy power plants). The configuration information of the agreement includes the total amount of electricity that the power plant needs to supply to the electricity-consuming enterprise, the proportion of different types of electricity (green electricity and thermal power), and the load demand of the electricity-consuming enterprise.

[0021] In actual operation, there are uncertainties in the load demand of electricity-consuming enterprises and the renewable energy output of renewable energy power plants. Traditional power configuration agreements are usually based on deterministic forecasts and cannot effectively cope with such uncertainties. Therefore, this embodiment uses a protocol simulation optimization method based on day-ahead plan decomposition and worst-case carbon flow accounting to determine the optimal protocol configuration strategy.

[0022] To guide actual operation, the configuration parameters of the power configuration protocol are first decomposed day-ahead to obtain the day-ahead dispatch plan. The specific steps include: Obtain the configuration information of the power configuration protocol to be simulated, including the total power consumption of the protocol, the power consumption ratio of the protocol, and the load demand of the protocol; Calculate the green electricity agreement amount and the thermal power agreement amount based on the total electricity amount under the agreement and the electricity proportion under the agreement; Based on the agreed load demand, a day-ahead decomposition method based on adjustment coefficients is used to decompose the green electricity and thermal power agreed quantities to obtain the time-series output data of the day-ahead scheduling plan. The time-series output data includes green electricity time-series output data and thermal power time-series output data, and the adjustment coefficients satisfy the balance constraints.

[0023] In this embodiment, the protocol load demand is time-series load data based on time periods, denoted as P. L (t) represents the contracted load demand during time period t, and the contracted load demand is the same as the total contracted electricity volume, meaning the total contracted electricity volume should meet the contracted load demand. Based on the total contracted electricity volume Q and the green electricity ratio c, the total electricity volume is divided into green electricity contracted volume Q. grAgreement on power generation Q between thermal power plants th That is, Q gr =Q*c, Q th =Q*(1-c). Traditional day-ahead decomposition methods are mainly based on simple averaging of historical data, without fully considering the time-series characteristics and uncertainties of load and renewable energy output. Therefore, this embodiment proposes a robust optimization-based day-ahead decomposition method that considers both contract fulfillment requirements and worst-case feasibility, ensuring that the day-ahead plan satisfies the configuration agreement while possessing sufficient robustness to cope with uncertainties. The day-ahead dispatch plan is divided into a day-ahead green power plan and a day-ahead thermal power plan. The day-ahead green power plan can be expressed as: In the formula, This represents the green electricity output data for time period t, where T represents the total time period of the configuration protocol. This represents the green electricity plan adjustment factor for time period t. This factor is adjusted appropriately based on renewable energy forecasts. For monthly power allocation agreements, the total number of execution periods T is... Hours. In this embodiment, green electricity refers to the electricity generated by renewable energy sources, and green electricity output is the output of renewable energy sources.

[0024] Day-ahead thermal power plans need to be coordinated with day-ahead green power plans to ensure that total power supply meets load demand. At the same time, as a regulating power source, thermal power needs to provide backup support for uncertainties in renewable energy output and load. A day-ahead thermal power plan can be represented as: In the formula, This represents the thermal power output data for time period t. The thermal power plan adjustment factor for time period t needs to be coordinated with the green power plan adjustment factor to ensure overall power balance.

[0025] The adjustment coefficient is introduced to address the differences in daily electricity demand and fluctuations in renewable energy output. Through subsequent simulation and optimization of these coefficients, the optimal allocation of the agreed-upon electricity can be achieved. The adjustment coefficient needs to satisfy balance constraints: The above two constraints ensure that the total day-ahead planned electricity volume still conforms to the power allocation agreement; adjustments are merely optimizations of the time-series distribution. Furthermore, to ensure the feasibility of the day-ahead plan in the face of uncertainty, it is necessary to consider that even under the most unfavorable combination of load and renewable energy output, the power system can still maintain power balance and meet safe operation requirements; that is, power balance constraints must be met. In the formula, U represents the set of joint uncertainty scenarios for load and renewable energy output. This represents the reserve capacity of the power system during time period t.

[0026] The above constraints mean that, in scenarios with uncertain output, the maximum difference between the agreed load demand and the day-ahead dispatch plan should be within the reserve capacity of the power system to ensure power balance.

[0027] To achieve optimal carbon reduction for the power configuration agreement (PCA), it is necessary to calculate the carbon emissions under the worst-case scenario, serving as the maximum benchmark for evaluating the PCA's carbon reduction effectiveness. The worst-case scenario refers to the combination of load demand and renewable energy output that maximizes the carbon emissions from thermal power plants. The carbon emissions under this scenario represent the worst environmental performance of the PCA's execution; essentially, it refers to the scenario where the load demand of electricity-consuming enterprises is maximized and the output of renewable energy power plants is minimized. The worst-case scenario can be obtained through scenario search of historical carbon emissions from thermal power plants under historical PCA execution scenarios, or through Monte Carlo simulation of the PCA's execution scenarios. The specific steps for determining this scenario are not limited here.

[0028] By performing carbon flow calculations on the power system under a preset worst-case scenario, the maximum carbon emissions of the electricity-consuming entities are calculated and used as carbon emission limit constraints for these entities. Specific steps include: Based on the load data of the power consumption side and the output data of the power generation side under the worst-case scenario, power flow calculation is performed on the actual power network of the power system to obtain the power flow distribution. Based on the power flow distribution, a countercurrent power flow tracking algorithm is used to distribute the carbon emissions of the power generation entity to the power consumption entity, thereby obtaining the basic total carbon emissions of the power consumption entity under the worst-case scenario, and using the basic total carbon emissions as the maximum carbon emissions of the power consumption entity.

[0029] In this embodiment, for the worst-case scenario, carbon emission flow theory is used for carbon flow calculation. Carbon emission flow is a virtual network flow dependent on power flow, flowing from the generation side to the load side along with the active power flow, quantifying the carbon emissions generated to maintain branch power flow. The carbon emissions of the power system originate from gases emitted by thermal power units. To distribute carbon emissions from the generation side to the consumption side, this embodiment uses a carbon emission allocation model based on carbon emission flow theory for carbon flow calculation. Specifically, based on the load demand, green electricity output (i.e., renewable energy output), and thermal power output under the worst-case scenario, power flow calculations are performed on the power system. To simplify the power flow calculation, this embodiment uses a lossless network model for carbon flow tracking to eliminate the impact of network losses on carbon flow allocation; that is, virtual nodes are added to the branches, and the negative values ​​of branch losses are equivalent to virtual power output. Since both the generation and consumption sides utilize the power grid infrastructure, network losses should be shared fairly to avoid unfairness caused by one side bearing the burden alone. Therefore, a two-way network loss sharing mechanism is introduced. Based on the lossless network model, network losses are allocated to the consumption and generation sides respectively according to a two-way sharing coefficient. Power flow calculation methods, such as the DC power flow method, are used to calculate the power flow distribution of the power system, obtaining the power flow distribution of the lossless network model. Based on the calculated power flow distribution, a reverse power flow tracking algorithm is used to allocate carbon emissions from the generation side to the user side, thereby obtaining the basic total carbon emissions of the main consumption entity, i.e., the electricity-consuming enterprise, under the worst-case scenario. This basic total carbon emissions is the maximum carbon emissions of the electricity-consuming enterprise. It should be noted that the specific calculation steps for carbon emissions in this embodiment can refer to the conventional calculation process of carbon emission allocation based on carbon emission flow theory, and will not be repeated here.

[0030] In a preferred embodiment, since deviations may occur during the actual execution of the power configuration agreement, these deviations directly affect the carbon emission reduction effect, meaning they lead to additional carbon emissions. Therefore, based on the total basic carbon emissions, the deviation carbon emissions can also be calculated to correct the maximum carbon emissions of the electricity-consuming enterprise. Specific steps include: Based on the difference between the load data of the power consumption side and the output data of the power generation side under the worst-case scenario and the corresponding historical load data and historical output data, a deviation network of the actual power network is established. Power flow calculation is performed on the deviation network, and based on the calculated power flow distribution, the reverse power flow tracking algorithm is used to distribute the deviation carbon emissions of the power generation side to the power consumption side, thereby obtaining the deviation carbon emissions of the power consumption side. The sum of the baseline total carbon emissions and the deviation carbon emissions shall be taken as the maximum carbon emissions of the electricity-consuming entity.

[0031] In this embodiment, the actual power network of the power system is decomposed into a basic network and a deviation network based on the network decomposition method. The basic network is the lossless network of the previous embodiment. The deviation network is represented by the difference between the historical average load data of the electricity-consuming entity and the load data of the electricity-consuming entity under the worst-case scenario, and the difference between the historical average output data of the generation entity and the output data of the generation entity under the worst-case scenario. That is, the load and output of the deviation network are both load deviation and output deviation. The calculation steps for carbon emission allocation under this network are the same as those for the basic total carbon emissions calculation in the previous embodiment. Specifically, power flow calculation based on network loss allocation is performed according to the load and output of the deviation network to obtain the power flow distribution of the deviation network. Based on the power flow distribution of the deviation network, an inverse power flow tracking algorithm is used to allocate the deviation carbon emissions of the generation entity to the electricity-consuming entity, obtaining the deviation carbon emissions of the electricity-consuming entity. Finally, the basic total carbon emissions of the electricity-consuming entity are taken as the maximum carbon emissions of the electricity-consuming entity. This method can improve the accuracy of carbon emission limit calculation results.

[0032] Since power configuration agreements involve multiple stakeholders, their decisions influence each other, forming complex strategic interactions. Traditional single-stakeholder optimization models cannot accurately depict these interactions, and traditional game theory models mainly consider deterministic environments and cannot cope with uncertain challenges. Therefore, this embodiment uses a hybrid game theory model to establish a multi-level agreement simulation optimization model to incorporate uncertain factors into the multi-stakeholder decision optimization framework.

[0033] Hybrid game theory is a hierarchical game model applicable to multi-agent interaction problems with a clear decision-making order. User enterprises, as the demand side of the power configuration agreement, first determine key parameters such as the agreed-upon electricity demand and the proportion of green electricity. Power plants then formulate corresponding configuration parameter strategies and supply plans based on the user enterprises' decisions. This decision-making order reflects the actual operating characteristics of the electricity market; user enterprises' energy demand is relatively stable, and power plants need to adjust their supply strategies according to demand. Based on the actual operating scenario, this embodiment's multi-level agreement simulation optimization model adopts a two-layer simulation optimization model with the electricity user as the leader and the power generation side as the follower. The optimization parameters of the model are the configuration information in the configuration agreement (including the total agreed-upon electricity volume, the agreed-upon electricity proportion, and the agreed-upon load demand). The total agreed-upon electricity volume and the agreed-upon electricity proportion are represented in the form of time-series output data from the day-ahead dispatch plan. The upper-layer simulation optimization model is the electricity user-side agreement simulation model, and the lower-layer simulation optimization models include thermal power-side agreement simulation models and green electricity-side agreement simulation models. The following sections provide a detailed description of each simulation model.

[0034] As the leader in the game, electricity-consuming enterprises need to formulate the optimal power allocation agreement strategy based on the power plant's response strategy. In this embodiment, the goal of the electricity-side agreement simulation model is to minimize the load deviation and carbon emissions of electricity-consuming enterprises while meeting electricity demand and carbon emission reduction constraints. The specific model construction steps include: Based on the agreed load demand, historical load demand, and load deviation tolerance threshold, the load deviation of the electricity consumption entity is obtained; Based on the agreed load demand and the time-series output data, carbon flow accounting and carbon emission allocation are performed on the power system to obtain the carbon emissions of the main electricity consumer. A simulation model of the electricity consumption protocol is constructed with the objective functions of minimizing the load deviation and the carbon emissions.

[0035] In this embodiment, the load deviation mainly stems from the uncertainty of electricity demand. That is, the electricity load is affected by various uncertainties such as production plan adjustments, equipment maintenance, and market demand fluctuations. To quantify and allocate the uncertainty risks during the execution of the power allocation agreement, a load deviation tolerance threshold is pre-set based on historical load data statistical analysis. This threshold represents the maximum tolerance range of the electricity-consuming enterprise for load deviation. Preferably, the load deviation tolerance threshold is represented by the product of the load deviation tolerance coefficient and the standard deviation of the load demand forecast error. The standard deviation of the load demand forecast error is obtained based on historical load data statistics, and the load deviation tolerance coefficient is used to control the conservatism of load demand uncertainty, with a value range of [0,3]. The specific value can be selected according to the actual situation of the electricity-consuming enterprise.

[0036] Based on the agreed load demand, historical load demand, and load deviation tolerance threshold, the load deviation P of the electricity user is obtained by statistically analyzing the deviations exceeding the load deviation tolerance threshold. open Its expression is: In the formula, This represents the historical load demand for time period t, also known as historical load data. Here, the historical average load demand value is used. This represents the protocol load demand during time period t. This represents the load deviation tolerance threshold for time period t, where T represents the total number of time periods. This represents a positive function, i.e. .

[0037] The carbon emissions of the electricity-consuming entity can be calculated using a carbon emission allocation model based on carbon emission flow theory. Specifically, the power flow distribution is calculated based on the time-series output data of the agreed load demand and the day-ahead scheduling plan, and the carbon emission allocation is performed using the reverse power flow tracking algorithm based on the power flow distribution, thereby obtaining the carbon emissions of the electricity-consuming entity. The specific calculation steps for carbon emissions are the same as those for the maximum carbon emissions in the previous embodiment, and will not be repeated here.

[0038] The electricity consumption agreement simulation model in this embodiment takes minimizing the load deviation and carbon emissions of the electricity-side entity as its objective functions. That is, the electricity consumption agreement simulation model is a multi-objective optimization simulation model. Preferably, the load deviation can also be directly represented by the difference between historical load demand and the agreed load demand. In addition, based on minimizing the load deviation and carbon emissions, the operating cost can also be minimized as another optimization objective to construct the objective function. The operating cost is represented by the sum of the products of the preset green electricity price and thermal power price and the corresponding green electricity agreement amount and thermal power agreement amount, respectively. In this case, the electricity consumption agreement simulation model takes minimizing the load deviation, carbon emissions and operating costs as its objective functions.

[0039] The constraints of the electricity consumption agreement simulation model include carbon emission limit constraints and electricity consumption load operation constraints. The electricity consumption load operation constraints include power load balance constraints, load boundary constraints, and load fluctuation constraints. Specifically, the carbon emission limit constraint means that the carbon emissions of the electricity consumption entity should be less than the maximum carbon emissions calculated under the worst-case scenario. The power load balance constraint means that the total power consumption under the agreement must meet the load demand of the electricity consumption enterprise; that is, the sum of the time-series load data in the agreement load demand (total demand value) should equal the total power consumption under the agreement. In reality, the uncertainty of load demand is reflected in the adjustment coefficients during the day-ahead plan decomposition. Therefore, the power load balance constraint can also be expressed as: In the formula, P L (t) represents the contracted load demand during time period t, c represents the percentage of green electricity, and Q represents the total contracted electricity volume. This is the green electricity plan adjustment factor for time period t. Let t be the thermal power plan adjustment factor for time period t, where T represents the total number of time periods.

[0040] Load boundary constraints refer to the requirement that the load demand of an electricity-consuming enterprise at any given time should not exceed its upper and lower load boundaries. Preferably, to reflect the uncertainty of the load, the output adjustment coefficient can also be combined with the load demand to represent the uncertainty of the load. Then the load boundary constraint can be expressed as: In the formula, These represent the lower and upper boundaries of the load demand, respectively.

[0041] Load fluctuation constraints refer to the requirement that the load fluctuation of an electricity-consuming enterprise in adjacent time periods should not exceed the maximum allowable fluctuation value, i.e. In the formula This represents the maximum load fluctuation. Preferably, the load demand for each time period can also be combined with the adjustment coefficient to represent the load fluctuation. In this case, the expression for the load demand in the constraint conditions can refer to the expression for the load demand in the preferred load boundary constraints, which will not be repeated here.

[0042] The thermal power plant agreement simulation model takes maximizing the performance of the thermal power generation entity as its objective function. The performance refers to the deviation between the actual output and the planned output of the thermal power plant. The smaller the output deviation, the higher the performance. Since the agreement design is the stage before the agreement execution, the actual output data cannot be obtained. Therefore, this embodiment uses historical average output data to represent the actual output. The performance is represented by the difference between the thermal power time-series output data in the day-ahead scheduling plan and the historical output data of thermal power. The objective function is to minimize the sum of the output deviations in each time period. Minimizing the output deviation is actually maximizing the performance.

[0043] The thermal power plant's protocol simulation model also needs to meet several constraints to ensure the safe and reliable operation of the thermal power plant under permissible uncertainties. Preferably, the constraints include unit operation constraints, ramping constraints, and minimum start-up and shutdown constraints. Among them, the unit operation constraint means that the unit output should not exceed the safe operating range of the unit. This constraint ensures that the output of the thermal power unit remains within the safe operating range under all possible load and wind power output uncertainty scenarios, avoiding over-limit operation due to uncertainty. The ramping constraint means that the output change between adjacent time periods does not exceed the physical ramping capacity of the unit, preventing equipment damage caused by rapid adjustment needs. The ramping constraint includes upward ramping constraints and downward ramping constraints. The minimum start-up and shutdown constraint means that the start-up and shutdown durations of the thermal power unit meet the minimum operating time requirements of the equipment, protecting the unit from equipment fatigue caused by frequent start-ups and shutdowns. In addition, a fuel supply constraint can be set, which means that the total fuel consumption of the thermal power plant does not exceed the upper limit of the fuel supply capacity, ensuring the reliability of fuel supply and the continuity of operation.

[0044] In a preferred embodiment, due to the uncertainty of renewable energy output, thermal power output will fluctuate accordingly. However, the fluctuation should be limited to a certain range to maintain stable output. Therefore, the performance of the objective function in the thermal power side agreement simulation model can also be expressed as the difference between the thermal power tolerance time-series output data and the historical output data of the thermal power generation entity. The thermal power tolerance time-series output data is calculated from the preset thermal power performance tolerance coefficient and the thermal power time-series output data of the day-ahead dispatch plan. The specific calculation process can be referred to the calculation process of thermal power performance deficiency in the next embodiment.

[0045] In another preferred embodiment, the step of constructing the thermal power-side protocol simulation model further includes: The reliability items for thermal power performance are calculated based on the difference between the historical output data of the main thermal power generation entity and the time-series output data of thermal power in the day-ahead dispatch plan. Based on the preset thermal power performance tolerance coefficient and the thermal power time-series output data of the day-ahead dispatch plan, the thermal power tolerance time-series output data is calculated, and the thermal power performance shortfall is calculated based on the difference between the thermal power tolerance time-series output data and the historical output data of the thermal power generation entity. The difference between the reliable thermal power performance items and the unsatisfactory thermal power performance items is taken as the performance level of the thermal power generation entity. A thermal power side protocol simulation model is constructed with maximizing the performance level of the thermal power generation entity as the objective function.

[0046] In this embodiment, the compliance status of thermal power plants is divided into two parts. One is the difference between the tolerable time-series output data of thermal power plants and the historical output data of the main thermal power generation entity. The tolerable time-series output data of thermal power plants can be understood as the minimum planned output of thermal power plants that can be tolerated. This output deviation indicates that there is still a deviation between the minimum planned output and the actual output of thermal power plants. The actual output still does not reach the agreed minimum planned output, that is, the compliance is insufficient. Therefore, the thermal power non-compliance item P can be calculated based on the deviation. pena_th : In the formula, This represents the thermal power output data for time period t in the day-ahead dispatch plan. This represents the historical power output data of a thermal power plant during time period t. The tolerance coefficient for thermal power plants is preferably between 0.05 and 0.1. The minuend of this formula is the tolerance time series data for thermal power plants.

[0047] In the previous embodiment, the thermal power plant's agreement simulation model actually uses the thermal power plant's non-compliance items to characterize the degree of compliance, and minimizes the non-compliance items to represent the maximization of the degree of compliance. However, in reality, thermal power plants may also experience situations where actual output exceeds planned output. This situation indicates that the thermal power plant can reliably fulfill its obligations, and its formula is expressed as: In the formula, P comp_th This indicates the reliability of thermal power plant performance; the value represents the degree to which the planned output settings enable the thermal power plant to reliably meet its performance obligations.

[0048] Then, the difference between the reliable items and the unreliable items of thermal power plant performance is taken as the performance level, and the thermal power plant side agreement simulation model is constructed with the maximization of the performance level as the objective function. The simulation goal of this model is to enable the thermal power plant to perform its planned output reliably to the greatest extent.

[0049] In addition to maximizing the degree of contract fulfillment, another optimization objective can be added: maximizing the revenue of thermal power plants. The revenue of thermal power plants is represented by the product of the preset thermal power price and the thermal power contracted amount. In this case, the thermal power side contract simulation model is a multi-objective simulation model.

[0050] Similarly, the green electricity side agreement simulation model also takes maximizing the degree of fulfillment as the objective function. The degree of fulfillment can be represented by the difference between the green electricity time-series output data in the day-ahead scheduling plan and the green electricity historical output data. The smaller the difference, the higher the degree of fulfillment.

[0051] In a preferred embodiment, since the intermittency and randomness of renewable energy output are objectively present, this embodiment characterizes the uncertainty of green electricity output by setting a green electricity performance tolerance coefficient. Based on the green electricity performance tolerance coefficient and the green electricity time-series output data of the day-ahead dispatch plan, the minimum planned output that the agreement can tolerate is calculated. If this minimum planned output is not met, it indicates that the renewable energy power plant is not performing its obligations. Therefore, the performance level of the objective function in the green electricity side agreement simulation model can also be expressed as the difference between the green electricity tolerance time-series output data and the historical output data of the green electricity power generation entity. The smaller the difference, the higher the performance level. For the specific calculation process, please refer to the calculation process for the green electricity performance deficiency item in the next embodiment.

[0052] In another preferred embodiment, the step of constructing the green electricity side protocol simulation model further includes: The reliability items for green power performance are calculated based on the difference between the historical output data of the main green power generation entity and the time-series output data of green power in the day-ahead dispatch plan. Based on the preset green electricity performance tolerance coefficient and the green electricity time-series output data of the day-ahead dispatch plan, calculate the green electricity tolerance time-series output data, and calculate the green electricity performance shortfall based on the difference between the green electricity tolerance time-series output data and the historical output data of the green electricity power generation entity. The difference between the reliable green electricity performance items and the insufficient green electricity performance items is taken as the performance level of the green electricity generation entity. A green electricity side protocol simulation model is constructed with the objective function of maximizing the performance level of the green electricity generation entity.

[0053] In this embodiment, the compliance status of renewable energy power plants is also divided into two parts: one is the green electricity non-compliance item, and the other is the green electricity compliance reliability item. Specifically, the non-compliance item refers to the fact that the tolerable minimum planned output of green electricity still exceeds the actual output of green electricity, and the actual output still fails to reach the agreed minimum planned output. The green electricity non-compliance item P pena_gr It can be represented as: In the formula, This represents the green power output data for time period t in the current dispatch plan. This represents the historical power output data of renewable energy power plants during time period t. The tolerance factor for renewable energy power plants is the minuend of the formula, which represents the green electricity tolerance time-series output data.

[0054] The performance reliability of green electricity is represented by the difference between actual output and planned output, and its formula is as follows: In the formula, P comp_gr This indicates the reliability of green energy performance; the value represents the degree to which the planned output configuration enables green power plants to reliably meet their performance obligations.

[0055] Then, the difference between the reliable green electricity performance items and the insufficient green electricity performance items is taken as the performance level, and the green electricity side agreement simulation model is constructed with the maximization of the performance level as the objective function. The simulation goal of this model is to enable the planned output of green power plants to be performed reliably to the greatest extent.

[0056] In addition to maximizing the degree of compliance, the optimization objective can be to maximize the revenue of green power plants (renewable energy power plants). The revenue of green power plants is represented by the product of the preset green electricity price and the green electricity agreement amount. Furthermore, the optimization objective can be to minimize the carbon emissions of renewable energy power plants. The carbon emissions can be calculated using a carbon emission sharing model. In this case, the green electricity agreement simulation model is also a multi-objective simulation model.

[0057] The constraints of the greenfield agreement simulation model include an output ceiling constraint and a total green electricity generation constraint. The output ceiling constraint means that the planned output of renewable energy units in any time period cannot exceed the actual renewable energy output capacity under all possible uncertainty scenarios, to ensure that the power generation plan of renewable energy units remains physically feasible in the face of the worst-case scenario. The total green electricity generation constraint means that the total power generation of renewable energy units must at least meet a preset proportion of the green electricity agreement amount, and its formula is expressed as: In the formula, Q grFor the amount of electricity covered by the Green Electricity Agreement, This represents the compliance tolerance factor for renewable energy power plants. The total green electricity generation constraint ensures that renewable energy power plants can basically fulfill their power allocation agreements and provide a reliable supply of green electricity.

[0058] It should be noted that the objective functions and constraints of the upper and lower layer models of the multi-level protocol simulation optimization model provided by the present invention can be flexibly selected according to actual conditions such as computational load and accuracy requirements. The above embodiments are preferred designs rather than complete limitations.

[0059] The multi-level protocol simulation optimization model constructed through the above embodiments has a two-layer structure. Its data form can be expressed as a nested two-layer optimization problem. The upper layer is the configuration decision of the power-consuming enterprise, and the lower layer is the equilibrium game between the power generation enterprises. The model can be solved by the conventional solution algorithm of the two-layer game model. There are no restrictions on its solution process here.

[0060] By solving the multi-level protocol simulation optimization model, the optimal values ​​of the parameters to be optimized in the model can be obtained, including the time-series output data of the day-ahead scheduling plan (including the planned output and adjustment coefficients of thermal power / green power) and the protocol load demand. Then, by summing the time-series output data, the protocol power volume of green power and thermal power can be obtained, thereby obtaining the optimal values ​​of the total protocol power volume and the protocol power ratio. That is, by reverse calculation, the optimal values ​​of the configuration parameters of the power configuration protocol can be obtained. Based on the optimal values ​​obtained from the simulation model, the simulation configuration strategy of the power configuration protocol can be obtained.

[0061] This embodiment provides a simulation optimization method for power allocation agreements based on carbon emission reduction. Through a robust day-ahead planning decomposition method, it effectively addresses the uncertainties in renewable energy output and load demand, improving the stability of power allocation agreement performance. A multi-level agreement simulation optimization model accurately models the strategic interactions among multiple power entities and the actual operating characteristics of the power market, enhancing the accuracy of simulation results and thus improving the feasibility of the power allocation agreement. Carbon emission allocation through carbon flow accounting quantifies the actual contribution of each entity to carbon emission reduction and incorporates carbon emissions into the optimization objective of the simulation model, achieving carbon emission reduction optimization of the power system and promoting its low-carbon transformation.

[0062] Please see Figure 2 Based on the same inventive concept, the second embodiment of this invention proposes a power configuration protocol simulation optimization system based on carbon emission reduction, comprising: The day-ahead decomposition module 10 is used to obtain the configuration information of the power configuration protocol to be simulated, perform day-ahead decomposition on the configuration information, and obtain the time-series output data of the day-ahead scheduling plan. The protocol body of the power configuration protocol includes the power consumption side body and the power generation side body. The power generation side body includes the thermal power generation body and the green power generation body. The carbon flow calculation module 20 is used to perform carbon flow calculation on the power system according to the worst-case scenario executed by the preset power configuration protocol, so as to obtain the maximum carbon emissions of the main body on the power consumption side. The protocol simulation module 30 is used to perform simulation optimization on the configuration information based on the time-series output data, the worst-case scenario, and a preset multi-level protocol simulation optimization model to obtain a simulation configuration strategy. The multi-level protocol simulation optimization model takes minimizing the load deviation and carbon emissions of the electricity-consuming entity, maximizing the compliance of the thermal power generation entity, and maximizing the compliance of the green power generation entity as optimization objectives, and takes carbon emission quota constraints based on the maximum carbon emissions, electricity-consuming load operation constraints, thermal power generation operation constraints, and green power generation operation constraints as constraints.

[0063] The technical features and effects of the carbon emission reduction-based power configuration protocol simulation optimization system proposed in this embodiment are the same as those of the method proposed in this embodiment, and will not be repeated here. Each module in the above-mentioned carbon emission reduction-based power configuration protocol simulation optimization system can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or it can be stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0064] Furthermore, embodiments of the present invention also propose a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0065] Please see Figure 3The diagram illustrates the internal structure of a computer device in one embodiment, which may specifically be a terminal or a server. The computer device includes a processor, memory, network interface, display, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a simulation optimization method for a carbon reduction-based power configuration protocol. The display screen may be an LCD screen or an e-ink display screen. The input devices may be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0066] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computing devices may include more or fewer components than those shown in the figure, or combine certain components, or have the same component arrangement.

[0067] In summary, the embodiments of this invention propose a method, system, and device for simulating and optimizing power configuration protocols based on carbon emission reduction. The method acquires the configuration information of the power configuration protocol to be simulated, performs day-ahead decomposition on the configuration information to obtain time-series output data for the day-ahead scheduling plan. The protocol subjects of the power configuration protocol include a power consumption side subject and a power generation side subject, with the power generation side subject including thermal power generation subjects and green power generation subjects. Based on a preset worst-case scenario for the execution of the power configuration protocol, carbon flow calculations are performed on the power system to obtain the maximum carbon emissions of the power consumption side subject. Based on the time-series output data and a preset multi-level protocol simulation optimization model, the configuration information is simulated and optimized to obtain a simulation configuration strategy. The multi-level protocol simulation optimization model aims to minimize the load deviation and carbon emissions of the power consumption side subject, maximize the compliance of the thermal power generation subject, and maximize the compliance of the green power generation subject, with constraints based on the maximum carbon emissions, power consumption side load operation constraints, thermal power generation operation constraints, and green power generation operation constraints. This invention employs a robust optimized day-ahead planning decomposition method to effectively address the uncertainties in renewable energy output and load demand, thereby improving the stability of power allocation agreement performance. By establishing a multi-level agreement simulation optimization model, it accurately models the strategic interactions among multiple power entities and the actual operational characteristics of the power market, improving the accuracy of simulation results and thus enhancing the feasibility of power allocation agreements. Furthermore, by using carbon flow accounting for carbon emission allocation, it quantifies the actual contribution of each entity to carbon reduction and incorporates carbon emissions into the optimization objective of the simulation model, thereby optimizing carbon reduction in the power system and promoting its low-carbon transformation.

[0068] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0069] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the technical principles of this invention, and these improvements and substitutions should also be considered within the scope of protection of this application. Therefore, the scope of protection of this patent application should be determined by the scope of the claims.

Claims

1. A simulation optimization method for power configuration protocols based on carbon emission reduction, characterized in that, include: The configuration information of the power configuration protocol to be simulated is obtained, and the configuration information is decomposed day-ahead to obtain the time-series output data of the day-ahead scheduling plan. The main body of the power configuration protocol includes the power consumption side body and the power generation side body. The power generation side body includes the thermal power generation body and the green power generation body. Based on the worst-case scenario executed according to the preset power configuration protocol, carbon flow accounting is performed on the power system to obtain the maximum carbon emissions of the main electricity consumer. Based on the time-series output data and the preset multi-level protocol simulation optimization model, the configuration information is simulated and optimized to obtain a simulation configuration strategy. The multi-level protocol simulation optimization model takes minimizing the load deviation and carbon emissions of the electricity-consuming entity, maximizing the compliance of the thermal power generation entity, and maximizing the compliance of the green power generation entity as optimization objectives, and takes carbon emission quota constraints based on the maximum carbon emissions, electricity-consuming load operation constraints, thermal power generation operation constraints, and green power generation operation constraints as constraints.

2. The simulation optimization method for power configuration protocols based on carbon emission reduction according to claim 1, characterized in that, The steps of obtaining the configuration information of the power configuration protocol to be simulated, and performing day-ahead decomposition on the configuration information to obtain the time-series output data of the day-ahead scheduling plan include: Obtain the configuration information of the power configuration protocol to be simulated, including the total power consumption of the protocol, the power consumption ratio of the protocol, and the load demand of the protocol; Calculate the green electricity agreement amount and the thermal power agreement amount based on the total electricity amount under the agreement and the electricity proportion under the agreement; Based on the agreed load demand, a day-ahead decomposition method based on adjustment coefficients is used to decompose the green electricity and thermal power agreed quantities to obtain the time-series output data of the day-ahead scheduling plan. The time-series output data includes green electricity time-series output data and thermal power time-series output data, and the adjustment coefficients satisfy the balance constraints.

3. The simulation optimization method for power configuration protocols based on carbon emission reduction according to claim 1, characterized in that, The step of calculating the carbon flow of the power system based on the worst-case scenario executed according to the preset power configuration protocol to obtain the maximum carbon emissions of the electricity-consuming entity includes: Based on the load data of the power consumption side and the output data of the power generation side under the worst-case scenario, power flow calculation is performed on the actual power network of the power system to obtain the power flow distribution. Based on the power flow distribution, a countercurrent power flow tracking algorithm is used to distribute the carbon emissions of the power generation entity to the power consumption entity, thereby obtaining the basic total carbon emissions of the power consumption entity under the worst-case scenario, and using the basic total carbon emissions as the maximum carbon emissions of the power consumption entity.

4. The simulation optimization method for power configuration protocols based on carbon emission reduction according to claim 3, characterized in that, Following the step of obtaining the base carbon emissions of the electricity-consuming entity under the worst-case scenario, the method further includes: Based on the difference between the load data of the power consumption side and the output data of the power generation side under the worst-case scenario and the corresponding historical load data and historical output data, a deviation network of the actual power network is established. Power flow calculation is performed on the deviation network, and based on the calculated power flow distribution, the reverse power flow tracking algorithm is used to distribute the deviation carbon emissions of the power generation side to the power consumption side, thereby obtaining the deviation carbon emissions of the power consumption side. The sum of the baseline total carbon emissions and the deviation carbon emissions is taken as the maximum carbon emissions of the electricity-consuming entity.

5. The simulation optimization method for power configuration protocols based on carbon emission reduction according to claim 2, characterized in that, The multi-level protocol simulation optimization model adopts a hybrid game model, which is constructed with the electricity consumption side as the leader and the power generation side as the follower. It includes an electricity consumption side protocol simulation model, a thermal power side protocol simulation model, and a green power side protocol simulation model. The electricity consumption side protocol simulation model takes minimizing the load deviation and carbon emissions of the electricity consumption side as the objective function, and carbon emission limit constraints and electricity consumption side load operation constraints as constraints. The electricity consumption side load operation constraints include power load balance constraints, load boundary constraints and load fluctuation constraints. The thermal power side agreement simulation model takes maximizing the performance of the thermal power generation entity as the objective function and thermal power generation operation constraints as the constraint conditions. The thermal power generation operation constraints include unit operation constraints, ramp-up constraints, and minimum start-up and shutdown constraints. The green electricity side agreement simulation model takes maximizing the performance of the green electricity generation entity as the objective function and green electricity generation operation constraints as the constraint conditions. The green electricity generation operation constraints include the upper limit constraint on output and the total green electricity generation constraint.

6. The simulation optimization method for power configuration protocols based on carbon emission reduction according to claim 5, characterized in that, The steps for constructing the power consumption-side protocol simulation model include: Based on the agreed load demand, historical load demand, and load deviation tolerance threshold, the load deviation of the electricity consumption entity is obtained; Based on the agreed load demand and the time-series output data, carbon flow accounting and carbon emission allocation are performed on the power system to obtain the carbon emissions of the main electricity consumer. A simulation model of the electricity consumption protocol is constructed with the objective functions of minimizing the load deviation and the carbon emissions.

7. The simulation optimization method for power configuration protocols based on carbon emission reduction according to claim 5, characterized in that, The steps for constructing the thermal power-side protocol simulation model include: The reliability items for thermal power performance are calculated based on the difference between the historical output data of the main thermal power generation entity and the time-series output data of thermal power in the day-ahead dispatch plan. Based on the preset thermal power performance tolerance coefficient and the thermal power time-series output data of the day-ahead dispatch plan, the thermal power tolerance time-series output data is calculated, and the thermal power performance shortfall is calculated based on the difference between the thermal power tolerance time-series output data and the historical output data of the thermal power generation entity. The difference between the reliable thermal power performance items and the unsatisfactory thermal power performance items is taken as the performance level of the thermal power generation entity. A thermal power side protocol simulation model is constructed with maximizing the performance level of the thermal power generation entity as the objective function.

8. The simulation optimization method for power configuration protocols based on carbon emission reduction according to claim 5, characterized in that, The steps for constructing the simulation model of the green electricity side protocol include: The reliability items for green power performance are calculated based on the difference between the historical output data of the main green power generation entity and the time-series output data of the green power in the day-ahead dispatch plan. Based on the preset green electricity performance tolerance coefficient and the green electricity time-series output data of the day-ahead dispatch plan, calculate the green electricity tolerance time-series output data, and calculate the green electricity performance shortfall based on the difference between the green electricity tolerance time-series output data and the historical output data of the green electricity power generation entity. The difference between the reliable green electricity performance items and the insufficient green electricity performance items is taken as the performance level of the green electricity generation entity. A green electricity side protocol simulation model is constructed with the objective function of maximizing the performance level of the green electricity generation entity.

9. A simulation and optimization system for power configuration protocols based on carbon emission reduction, characterized in that, include: The day-ahead decomposition module is used to obtain the configuration information of the power configuration protocol to be simulated, and to perform day-ahead decomposition on the configuration information to obtain the time-series output data of the day-ahead scheduling plan. The protocol body of the power configuration protocol includes the power consumption side body and the power generation side body. The power generation side body includes the thermal power generation body and the green power generation body. The carbon flow calculation module is used to perform carbon flow calculation on the power system based on the worst-case scenario executed according to the preset power configuration protocol, so as to obtain the maximum carbon emissions of the main body on the power consumption side. The protocol simulation module is used to perform simulation optimization on the configuration information based on the time-series output data, the worst-case scenario, and a preset multi-level protocol simulation optimization model to obtain a simulation configuration strategy. The multi-level protocol simulation optimization model takes minimizing the load deviation and carbon emissions of the electricity-consuming entity, maximizing the compliance of the thermal power generation entity, and maximizing the compliance of the green power generation entity as optimization objectives, and takes carbon emission quota constraints based on the maximum carbon emissions, electricity-consuming load operation constraints, thermal power generation operation constraints, and green power generation operation constraints as constraints.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

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